Composite SaaS scaling in cloud computing using a hybrid genetic algorithm

Mohd Yusoh, Zeratul Izzah & Tang, Maolin (2014) Composite SaaS scaling in cloud computing using a hybrid genetic algorithm. In Proceedings of the IEEE Congress on Evolutionary Computation, IEEE, Beijing International Convention Center, Beijing, China, pp. 1609-1616.

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A Software-as-a-Service or SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. Components in a composite SaaS may need to be scaled – replicated or deleted, to accommodate the user’s load. It may not be necessary to replicate all components of the SaaS, as some components can be shared by other instances. On the other hand, when the load is low, some of the instances may need to be deleted to avoid resource underutilisation. Thus, it is important to determine which components are to be scaled such that the performance of the SaaS is still maintained. Extensive research on the SaaS resource management in Cloud has not yet addressed the challenges of scaling process for composite SaaS. Therefore, a hybrid genetic algorithm is proposed in which it utilises the problem’s knowledge and explores the best combination of scaling plan for the components. Experimental results demonstrate that the proposed algorithm outperforms existing heuristic-based solutions.

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ID Code: 70356
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Genetic Algorithm, Cloud Computing, Software as a Service (SaaS), Clustering
DOI: 10.1109/CEC.2014.6900614
ISBN: 978-1-4799-6626-4
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500) > Distributed Computing not elsewhere classified (080599)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Please consult the authors
Deposited On: 22 Apr 2014 05:22
Last Modified: 31 Oct 2014 11:34

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